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Scikit-learn VS Scratchpad

Compare Scikit-learn VS Scratchpad and see what are their differences

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Scratchpad logo Scratchpad

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  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Scratchpad Landing page
    Landing page //
    2023-09-25

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Scratchpad videos

Lofi Plugin Review l Scratchpad Review l 2023

More videos:

  • Review - SCRATCHPAD | LO-FI SYNTH GRATIS | Muy Funcional con buenos PRESETS | VST REVIEW
  • Review - SCRATCHPAD by Patent Sound | REVIEW, ALL PRESETS + BEAT | FREE lofi VST Plugin

Category Popularity

0-100% (relative to Scikit-learn and Scratchpad)
Data Science And Machine Learning
Sales
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Productivity
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Scratchpad

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

Scratchpad Reviews

We have no reviews of Scratchpad yet.
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Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Scratchpad. It has been mentiond 29 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scikit-learn mentions (29)

  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 4 days ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 3 months ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago
  • WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
    The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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Scratchpad mentions (3)

  • Ask HN: Who is hiring? (January 2023)
    Scratchpad (https://scratchpad.com/) | REMOTE (US/Canada) | Full-time | Senior Backend Engineer, Senior Software Engineer - Backend Apply here: https://www.scratchpad.com/careers?ashby_jid=7b986939-6b0d-4365-98f8-9df4c6d83002 About Scratchpad At Scratchpad, our mission is to make salespeople happy. We believe we can do that by reducing complexity and creating delightful experiences, which has led us to pioneer the... - Source: Hacker News / over 1 year ago
  • Spreadsheets in Sales
    You should definitely check out: https://scratchpad.com/. Source: over 2 years ago
  • Show HN: Tara AI 2.0 – Fast project management with built-in automation
    Congrats on the new launch Iba and Syed, I still remember meeting you guys years ago in LA, you've come a long way! I'll echo some of the other comments here that most of the bigger companies I've worked with want a better JIRA but are weary of alternatives, data security, and the switching costs involved. Is there a way to offer this on top of the existing Atlassian tools? A similar model would be... - Source: Hacker News / almost 3 years ago

What are some alternatives?

When comparing Scikit-learn and Scratchpad, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Forecastio AI - Gain actionable sales pipeline insights, build accurate sales forecasts, automate sales planning, and forget about complex, time-consuming spreadsheets.

OpenCV - OpenCV is the world's biggest computer vision library

Aviso - Aviso provides predictive analytics software to help sales organizations optimize their performance and exceed revenue goals using machine learning algorithms and portfolio management techniques.

NumPy - NumPy is the fundamental package for scientific computing with Python

Clari - Clari is a predictive analytics platform for sales people.